The advantages and disadvantages of repeated measures

December 1, 2011

A repeated-measures design is using the same participants for all of your experimental conditions (Field, 2011). This is in contrast to an independent groups design, in which you have different groups of participants for the different experimental conditions so that each participant is exposed to just one condition (Howitt & Cramer, 2011).

A benefit of using repeated-measures (using the same participants for both manipulations) is it allows the researcher to exclude the effects of individual differences that could occur if two different people were used instead (Howitt & Cramer, 2011). Factors such as IQ, ability, age and other important variables remain the same in repeated-measures as it is the same person taking part in each condition (Field, 2011). This is one of the disadvantages of using independent groups.

Another benefit of repeated-measures is it requires fewer participants than independent groups. For example, whereas repeated-measures may only need 20 participants, independent groups would need 20 participants for each condition (Howitt & Cramer, 2011). Repeated-measures are therefore quicker and easier to successfully recruit the necessary number of participants.

Although these strengths favour repeated-measures, independent groups do have strengths where repeated-measures weaken. For example, using the same participants for all conditions leads to difficulties counteracting problems of order effects (performance changing due to the order of exposure to conditions). Results could therefore be due to boredom effecting concentration and performance in reaction times or accuracy (Pan, Shell & Schleifer, 1994; Bergh & Vrana, 1998). Effects could also be due to repetition causing participants’ results to improve because they were given more chance to practice and become familiar with the task (Collie, Maruff, Darby & McStephen, 2003).

Order effects can be reduced by counterbalancing (Field, 2011). For example, half the participants would be exposed to control A first and then control B, and the other half of participants exposed to control B and then control A (Howitt & Cramer, 2011). The results should then be less affected by factors such as boredom and practice. Additionally, researchers can also provide breaks during the experiment to counteract boredom and loss of concentration (Pan, Shell & Schleifer, 1994).

Another weakness of repeated-measures is the need for additional experimental materials. For example, if a study was testing how Factor A and Factor B affected participants’ memory for learning lists, in repeated-measures the researcher would require a different list of words for participants to memorise for both Factor A and B, whereas in independent groups the same list could be used for each factor because each group only sees the material once (Nilsson, Soli & Sullivan, 1994). Therefore, in using repeated-measures the individual differences of participants are reduced but this instead produces problems with individual differences between the materials participants are exposed to. Therefore results may be due to these differences in materials rather than the independent variable in question. The materials must therefore be carefully examined to ensure equal quality in factors such as difficulty (Riedel, Klaassen, Deutz, Someren & Praag, 1999).

Therefore there are advantages and disadvantages for both repeated-measures and independent groups. Whereas repeated-measures are good for quick recruitment, less participants and removing individual differences between participants, independent groups are good for avoiding order effects and differences in experimental materials. Each study must have careful consideration into which design would best meet the needs of the study. Once chosen, the problems related to the design (which both have) must be reduced to have as little effect on results as possible.

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Agreeing with dsowen, the type of design used should be dependent upon the specific research question. Sackett & Wennberg (1997), suggest that the research design is entirely dependent on the question asked and as a result, it could be assumed neither could be seen as a better option for research. Both independent and dependent designs have their weaknesses. Repeated measures can lead to positive bias (Vasey & Thayer, 1987) because results may not be valid due to demand characteristics and order effects/boredom as described by dsowen. Also, any incorrect use of supposition related to variance or covariance can mean too many null hypotheses are rejected (McCall & Appelbaum, 1973). Finally, participants often drop out prematurely from longitudinal studies (Little, 1995) and this can lead to unbalanced results in repeated measure designs.

Another advantage for repeated measures is that the results can be directly compared, which can be problematic for independent measures. However, it can suffer from demand characteristics, as the participants may realise what the experiment is about once they begin the second and see how it differs from the first. If a participant knows what the experiment is about they may behave how they think they expected to, rather than how they would normally.

Another design which combines the advantages of both independent and repeated measures is matched pairs design; matching every participant in one group with a very similar person in the other group. This allows the groups to be directly comparable and eliminates order and practice effects, whilst still controlling individual differences.

There are ways of dealing with the weaknesses of repeated measures. The biggest weakness of the repeated measures design is the order of conditions and how this may affect participant performance. A way of dealing with this is counterbalancing. This ensures that each condition is tested first and second in equal amounts. This usually involves the sample being split into two groups, one group does condition A first and then condition B whilst the second group would do condition B first then condition A.
Another weakness of the repeated measures design is that one condition may be harder than the other condition. To overcome this, experimenters must ensure that tests are equivalent. For example if participants were given to list of words to learn, to see whether memory recall was better in the afternoon or in the morning, a list of words would be created, all words being of similar length etc. these words would then be randomly allocated to two lists ensuring that both lists were equivalent.